Build AI Systems That Ship
Start this planThe overall fitness of model outputs — factual correctness, relevance, coherence, format compliance — for the user's need; the central mediator between design levers and outcomes. (Compounding value, trust, and safety at scale.)
started 0 · finished 0 (claimed) · not yet measured (verified) · no data (n<5)
Ordered tasks (49) — this is what a project auto-creates
- #1Prompt Engineering QualityHighTo Do
Shape model behavior through the input itself — roles, instructions, examples, and decoding — before you reach for weights or retrieval.
Assignee: Unassigned · due 0 days after project start · takes 14 days
- #2Model & Architecture SelectionHighTo Do
Choose the model family, scale, and provider that set the hard ceiling on capability, latency, and cost before any tuning begins.
Assignee: Unassigned · due 14 days after project start · takes 14 days
- #3Model Output Quality & AccuracyHighTo Do
Define how you measure the thing everything else feeds into — the actual quality of predictions and generations on held-out and live inputs.
Assignee: Unassigned · due 28 days after project start · takes 14 days
- #4Problem Framing & Business AlignmentMediumTo Do
Focus on about converting a fuzzy business ask into a measurable ML task worth building. It gives you the framing decisions that determine whether any downstream engineering matters.
Assignee: Unassigned · due 42 days after project start · takes 7 days
- #5Compute & Hardware ResourcesMediumTo Do
Focus on about how hardware availability constrains what you can train and serve, and how it interacts with inference optimization. It helps you plan around compute as a hard boundary, not an afterthought.
Assignee: Unassigned · due 49 days after project start · takes 7 days
- #6Retrieval-Augmented Generation & Context ConstructionHighTo Do
Feed the model current, domain-specific facts at inference time so its answers rest on retrieved evidence rather than stale training memory.
Assignee: Unassigned · due 56 days after project start · takes 14 days
- #7Context Grounding & Knowledge AccessHighTo Do
Focus on about whether the model actually uses the context you gave it, versus overriding it with confident parametric guesses.
Assignee: Unassigned · due 70 days after project start · takes 14 days
- #8Finetuning & Domain SpecializationHighTo Do
When adapting the model's weights beats prompting or retrieval, and how to do it without destroying general capability.
Assignee: Unassigned · due 84 days after project start · takes 14 days
- #9Training Data Quality, Coverage & VolumeHighTo Do
Address the cleanliness, coverage, volume, and predictive power of your training data. You learn why data work, not model work, drives most quality gains.
Assignee: Unassigned · due 98 days after project start · takes 14 days
- #10Feature Engineering QualityHighTo Do
Turning raw data into informative, reliable features — still decisive for tabular and classical ML systems that ship alongside LLM components.
Assignee: Unassigned · due 112 days after project start · takes 14 days
- #11Data Engineering & ManagementHighTo Do
Address the plumbing — sourcing, labeling, validating, versioning, and governing the data that feeds every model you train or evaluate.
Assignee: Unassigned · due 126 days after project start · takes 14 days
- #12Model Capacity & ComplexityHighTo Do
How a model family's expressive power interacts with your data volume to determine whether you underfit, overfit, or generalize.
Assignee: Unassigned · due 140 days after project start · takes 14 days
- #13Regularization & Overfitting ControlHighTo Do
How regularization constrains model complexity to control variance. You learn to read the symptoms that tell you which direction to move.
Assignee: Unassigned · due 154 days after project start · takes 14 days
- #14Training & Optimization DynamicsHighTo Do
The mechanics of getting training to converge — loss design, initialization, normalization, learning rate, and gradient stability.
Assignee: Unassigned · due 168 days after project start · takes 14 days
- #15Hyperparameter Tuning & Model SelectionHighTo Do
The disciplined search over non-learned settings — regularization strength, learning rate, decoding params — that selects the best-generalizing configuration.
Assignee: Unassigned · due 182 days after project start · takes 14 days
- #16Generalization PerformanceHighTo Do
The central objective — performance on unseen data — and the four levers (data, capacity, regularization, tuning) that move it.
Assignee: Unassigned · due 196 days after project start · takes 14 days
- #17Hallucination Reduction & Factual ConsistencyHighTo Do
Address keeping outputs faithful to source facts — minimizing confident fabrication that survives review because it reads as fluent.
Assignee: Unassigned · due 210 days after project start · takes 14 days
- #18Generative Model Quality & DiversityHighTo Do
Separates the two failure modes of generative systems — unrealistic outputs and collapsed diversity — and shows why you must measure both before shipping.
Assignee: Unassigned · due 224 days after project start · takes 14 days
- #19Uncertainty QuantificationMediumTo Do
Make your model report how confident it should be, separating irreducible noise from things it simply hasn't learned.
Assignee: Unassigned · due 238 days after project start · takes 7 days
- #20Agent & Multi-Agent Architecture DesignHighTo Do
Decide between a single agent and a coordinated multi-agent system, and how to structure the orchestration loop that drives planning and tool use.
Assignee: Unassigned · due 245 days after project start · takes 14 days
- #21Tool Integration & Action SpaceHighTo Do
Define, describe, and expose tools so an agent can reliably choose and invoke them, including interoperability protocols like MCP and A2A.
Assignee: Unassigned · due 259 days after project start · takes 14 days
- #22Agent Reasoning & Planning CapabilityHighTo Do
Elicit and structure reliable goal decomposition, multi-step planning, and coherent tool selection from an agent.
Assignee: Unassigned · due 273 days after project start · takes 14 days
- #23Agent Memory & StateHighTo Do
Retain conversational context within a session and persist knowledge across sessions so the agent acts on what it already knows.
Assignee: Unassigned · due 287 days after project start · takes 14 days
- #24Agent Reflection, Error Recovery & LearningHighTo Do
Give an agent the ability to check its own progress, recover from failures mid-task, and improve from feedback over time.
Assignee: Unassigned · due 301 days after project start · takes 14 days
- #25Agent Autonomy & Action SafetyHighTo Do
Calibrate how much independent action an agent takes against the guardrails, approval gates, and failure modes that keep it safe.
Assignee: Unassigned · due 315 days after project start · takes 14 days
- #26Task / Problem-Solving PerformanceHighTo Do
Define how to measure whether your agent actually completes complex, multi-step tasks — success, accuracy, and efficiency end-to-end.
Assignee: Unassigned · due 329 days after project start · takes 14 days
- #27Deployment & Serving ArchitectureHighTo Do
Choosing deployment patterns and serving frameworks — online vs. batch, edge vs. cloud, containers vs. serverless — that meet your latency and cost constraints.
Assignee: Unassigned · due 343 days after project start · takes 14 days
- #28Inference Optimization & EfficiencyHighTo Do
The model- and system-level techniques — quantization, compression, batching — that cut latency and compute cost enough to make production viable.
Assignee: Unassigned · due 357 days after project start · takes 14 days
- #29Cost & Resource EfficiencyHighTo Do
Understanding and controlling the real operating cost of your system — tokens, API spend, and deployment compute — and designing to minimize waste.
Assignee: Unassigned · due 371 days after project start · takes 14 days
- #30Monitoring & ObservabilityHighTo Do
Instrument production AI with tracing, logging, and alerting on the signals — drift, quality, cost — that reveal system health and degradation.
Assignee: Unassigned · due 385 days after project start · takes 14 days
- #31Evaluation & Validation RigorHighTo Do
Build an evaluation pipeline you can trust to make ship/no-ship decisions, and to power fast iteration.
Assignee: Unassigned · due 399 days after project start · takes 14 days
- #32MLOps Infrastructure & Pipeline AutomationMediumTo Do
Use the plumbing that turns ad-hoc model experiments into a repeatable delivery pipeline. You will learn what to standardize first and what to defer.
Assignee: Unassigned · due 413 days after project start · takes 7 days
- #33System Reliability & RobustnessHighTo Do
Make production behavior consistent, robust, and repairable under real traffic, drift, and failure. You get the operational levers that keep a shipped AI system running rather than the model metrics that got it deployed.
Assignee: Unassigned · due 420 days after project start · takes 14 days
- #34System ScalabilityMediumTo Do
Keep cost and latency flat as traffic, data, and the number of deployed models grow. It focuses on the scaling axis most teams ignore: model count.
Assignee: Unassigned · due 434 days after project start · takes 7 days
- #35Maintainability & ReproducibilityMediumTo Do
Focus on about whether an engineer who didn't build the system can debug, reproduce, and safely change it a year later. It treats maintainability as a first-class deliverable, not documentation hygiene.
Assignee: Unassigned · due 441 days after project start · takes 7 days
- #36Explainability & TransparencyMediumTo Do
Making model and agent decisions inspectable to the humans who must trust, debug, or contest them. It distinguishes explanations that aid engineers from those that aid users and regulators.
Assignee: Unassigned · due 448 days after project start · takes 7 days
- #37Developer Productivity & VelocityMediumTo Do
Focus on about the speed and friction of your build-iterate-ship loop — the tooling and feedback latency that governs how fast the team moves.
Assignee: Unassigned · due 455 days after project start · takes 7 days
- #38Continual Learning & Data FlywheelHighTo Do
Building the feedback loops and infrastructure for safe, frequent model updates driven by new data, drift, and user feedback — the data flywheel.
Assignee: Unassigned · due 462 days after project start · takes 14 days
- #39Data Distribution Shift & DriftMediumTo Do
Focus on about detecting when production data drifts from what the model learned, and keeping the model fresh enough to keep working.
Assignee: Unassigned · due 476 days after project start · takes 7 days
- #40Security, Guardrails & Safety ControlsHighTo Do
Define the layered defenses an LLM or agent system needs against misuse, injection, and leakage. It gives you the ordering of controls and where single layers fail.
Assignee: Unassigned · due 483 days after project start · takes 14 days
- #41Responsible AI, Fairness & EthicsHighTo Do
Building fairness, privacy, and harm mitigation into design and deployment rather than auditing for them afterward. You get the practices that make the system defensible to users and regulators.
Assignee: Unassigned · due 497 days after project start · takes 14 days
- #42Developer Vigilance & Verification DisciplineLowTo Do
Address the human rigor required when AI generates your code and artifacts. It is about resisting the pull to accept plausible output without verification.
Assignee: Unassigned · due 511 days after project start · takes 3 days
- #43Team, Organization & Change ManagementMediumTo Do
The people and process changes that let AI systems actually get adopted and maintained. It treats reliability as partly an organizational property, not just a technical one.
Assignee: Unassigned · due 514 days after project start · takes 7 days
- #44User Trust, Adoption & SatisfactionHighTo Do
Focus on about why users continue (or abandon) using your system, and how trust converts to business value. It links output quality, reliability, and fairness to the human decision to rely on the system.
Assignee: Unassigned · due 521 days after project start · takes 14 days
- #45Business Value & ImpactHighTo Do
Define the outcome the whole system exists to produce: real-world impact and user task success. You get how to measure value in terms of decisions changed and tasks completed, not model metrics.
Assignee: Unassigned · due 535 days after project start · takes 14 days
- #46Human-Centered & Collaboration DesignMediumTo Do
Designing the human-AI interaction — autonomy levels, interfaces, handoffs — so the system augments people. It focuses on the collaboration surface, not the model itself.
Assignee: Unassigned · due 549 days after project start · takes 7 days
- #47AI Ecosystem & Power DynamicsLowTo Do
Situates your system within the macro forces — vendor competition, compute concentration, geopolitics — that shape what you can build and depend on. It is about strategic exposure, not code.
Assignee: Unassigned · due 556 days after project start · takes 3 days
- #48Existential Safety & Value AlignmentHighTo Do
Apply a working stance on building AI whose objectives stay corrigible and deferential even as capability grows, framed for systems you actually ship rather than hypothetical superintelligence.
Assignee: Unassigned · due 559 days after project start · takes 14 days
- #49Scaling Laws & Network StructureHighTo Do
The power-law relationships that let you predict how model performance and cost move as you grow parameters, data, and compute — and when growth stops paying off.
Assignee: Unassigned · due 573 days after project start · takes 14 days